There are many types of apparent diseases in concrete bridges, and each type of disease has different characteristics such as shape, size, color, etc. A machine vision detection method for surface defects of concrete bridges based on laser scanning is proposed. Obtain surface data of concrete bridges through laser scanning equipment and convert it into two-dimensional images through rendering technology. Sharpen the edge areas of the image to make the image quality more natural and clear. Locate the region of interest in the apparent image of concrete bridges, and fuse the gradient direction histogram features extracted in the region of interest with local binary pattern features to form a set of concrete bridge apparent samples. SVM is used as the classifier, and the fused features are used for offline training of the classifier. Based on the training results, concrete bridge apparent disease detection is carried out. The experimental results show that the proposed method has a maximum fuzzy coefficient of 0.98 and a maximum quality index of 0.99, which is close to the optimal value of 1. This indicates that it can effectively improve the quality of concrete bridge surface images and obtain more accurate results of concrete bridge surface disease detection.